The rapid development of the low-altitude economy (LAE) has significantly enhanced the service diversity of the Internet of Things (IoT) networks, necessitating efficient coordination among the unmanned aerial vehicles (UAVs). In this work, we utilize multiple UAVs to provide both wireless data collection (WDC) service and wireless energy transfer (WET) service, and divide the IoT devices into the I-devices that only need the WDC service and the E-devices that only need the WET service, respectively, from the multiple UAVs. Due to their conflicting service demands on the UAVs with limited resources, we formulate the bi-objective optimization problem (BOOP) to minimize the age of information (AoI) for the I-devices and the hungry-level of energy (HoE) for the E-devices at the same time, by jointly optimizing all the UAVs' trajectories and WET decisions over time slots and their WDC decisions over sub-slots. To efficiently solve the complex BOOP, we innovatively transform it into a single-objective optimization problem (SOOP), in which the two conflicting objectives are scalarized via a self-adaptive objective weight. Unlike the conventional approach reliant on fixed and pre-defined objective weights, we optimize the objective weight jointly with other decision variables, enabling automatic adaptation to various network environments without human intervention. However, the proposed SOOP is NP-hard with a large number of decision variables. Accordingly, we propose a new Multi-Agent Adaptive and Hierarchical Deep Reinforcement Learning (MA$^{2}$HDRL) framework, which leverages a central controller (CC) to guide the local training of multiple individual UAV agents. In this framework, each UAV agent employs a two-tier hierarchical DRL model: tier-1 optimizes the trajectory and WET policies over the time slots, while tier-2 optimizes the WDC policy across the sub-slots. Meanwhile, the CC trains the global reward preference for all the UAV agents over the training episodes, to adaptively balance the WDC and WET service demands. Finally, extensive simulation results are conducted to demonstrate the outstanding performance of the proposed MA$^{2}$HDRL approach as compared to state-of-the-art benchmarks.

WiFi has been widely used for local area networking of devices and Internet access in the past two decades. Many researchers exploit WiFi signals for target sensing through analyzing the Channel State Information (CSI) of signals affected by the target movement. With the development of 6 G Integrated Sensing and Communication (ISAC), some researchers further consider using communication data packets for WiFi sensing. However, all the current works do not analyze the impact of ubiquitous interference on WiFi sensing performance. In this paper, we propose IRSensing, an interference-resistance design to improve the CSI quality under interference in the ISAC scenario, aiming to improve the WiFi sensing performance. IRSensing exploits the overall WiFi packet for CSI optimization. It first measures the interference level of each subcarrier based on variance analysis, then proposes a CSI optimization method based on maximal ratio combining to improve the CSI quality. It finally proposes a practical CSI enhancement process to adapt to complex interference situations in actual networks. We implement IRSensing on a hardware testbed and evaluate its performance under different settings. Experiment results show that it can significantly decrease the activity detection error rate by up to 80% and improve the classification accuracy by up to 15$\%$.

This paper proposes a novel scan-then-Iocalize scheme for the unmanned aerial vehicle (UAV) to localize a wireless sensor (WS). In both of the scan and the localizing phases, the UAV estimates its range and/or radial speed to the WS, by exploiting the orthogonal frequency division multiplexing (OFDM) sensing signal. We first derive the UAV's maximally allowable sensing radius to the WS to assure a sufficiently high correct estimation probability. Next, in the scan phase, we design the UAV's scan trajectory along a series of waypoints, where the number of the waypoints and their locations are found by solving the classic region least circle coverage problem. The scan phase ends when the UAV's estimated horizontal range to the WS becomes no larger than the maximally allowable sensing radius. The UAV then begins the localizing phase, where unlike the traditional three-measurement based localization, we propose that only two measurements of the UAV's range and radial speed to the WS are sufficient to localize the WS, but may generate a falsely-found ghost WS. By further identifying the UAV's improper flight angles that generate the ghost WSs, we newly propose the two-measurement based accurate localization algorithm. Finally, various simulation results are provided to validate the localizing performance of our proposed scheme.

This paper proposes a novel design on the wireless powered communication network (WPCN) in dynamic environments under the assistance of multiple unmanned aerial vehicles (UAVs). Unlike the existing studies, where the low-power wireless nodes (WNs) often conform to the coherent harvest-then-transmit protocol, under our newly proposed double-threshold based WN type updating rule, each WN can dynamically and repeatedly update its WN type as an E-node for non-linear energy harvesting over time slots or an I-node for transmitting data over sub-slots. To maximize the total transmission data size of all the WNs over$T$slots, each of the UAVs individually determines its trajectory and binary wireless energy transmission (WET) decisions over times slots and its binary wireless data collection (WDC) decisions over sub-slots, under the constraints of each UAV's limited on-board energy and each WN's node type updating rule. However, due to the UAVs’ tightly-coupled trajectories with their WET and WDC decisions, as well as each WN's time-varying battery energy, this problem is difficult to solve optimally. We then propose a new multi-agent based hierarchical deep reinforcement learning (MAHDRL) framework with two tiers to solve the problem efficiently, where the soft actor critic (SAC) policy is designed in tier-1 to determine each UAV's continuous trajectory and binary WET decision over time slots, and the deep-Q learning (DQN) policy is designed in tier-2 to determine each UAV's binary WDC decisions over sub-slots under the given UAV trajectory from tier-1. Both of the SAC policy and the DQN policy are executed distributively at each UAV. Finally, extensive simulation results are provided to validate the outweighed performance of the proposed MAHDRL approach over various state-of-the-art benchmarks.

This paper proposes a new on-demand wireless energy transfer (WET) scheme of multiple unmanned aerial vehicles (UAVs). Unlike the existing studies that simply pursuing the total or the minimum harvested energy maximization at the Internet of Things (IoT) devices, where the IoT devices' own energy requirements are barely considered, we propose a new metric called the hungry-level of energy (HoE), which reflects the time-varying energy demand of each IoT device based on the energy gap between its required energy and the harvested energy from the UAVs. With the purpose to minimize the overall HoE of the IoT devices whose energy requirements are not satisfied, we optimally determine all the UAVs' trajectories and WET decisions over time, under the practical mobility and energy constraints of the UAVs. Although the proposed problem is of high complexity to solve, by excavating the UAVs' self-attentions for their collaborative WET, we propose the multi-agent graph reinforcement learning (MAGRL) based approach. Through the offline training of the MAGRL model, where the global training at the central controller guides the local training at each UAV agent, each UAV then distributively determines its trajectory and WET based on the well-trained local neural networks. Simulation results show that the proposed MAGRL-based approach outperforms various benchmarks for meeting the IoT devices' energy requirements.

Abstract This paper applies the wireless powered communication network (WPCN) to an indoor communication system with two energy harvesting (EH) enabled users. Unlike the existing WPCN works designed for outdoor communications, where each user harvests energy only from the signals transmitted by a dedicatedly deployed hybrid access point (H‐AP), due to the short device‐to‐device distance in the indoor scenario, each user additionally harvests a sufficient amount of energy from the information signals transmitted by the H‐AP and the other user. First, the joint downlink and uplink throughput are maximized for the wireless powered indoor communication system. This problem is non‐convex. Thus, the authors manage to transform this problem into a convex one and solve it using convex optimization techniques. The solutions reveal that the total throughput increases largely over a reduced device‐to‐device distance due to the resultant harvested energy from both energy and information signals at each user. However, an unfair downlink versus uplink rate allocation phenomenon is observed. Thus, considering the importance of uplink communication quality for various indoor applications, a new problem is further proposed to maximize the uplink sum‐throughput over both users with an additional constraint to ensure a sufficient downlink rate. This problem is also shown to be non‐convex and is solved optimally by using a method similar to that in the first problem. Numerical results demonstrate the effectiveness of the proposed approach for improving the downlink versus uplink rate allocation fairness in the indoor wireless‐powered communication system.

In this paper, we investigate the uplink communication of a reconfigurable intelligent surface (RIS) assisted system, in which an user equipment (UE) with single radio frequency (RF) chain delivers information to an access point (AP) by adopting the spatial modulation (SM). Specifically, we first investigate the transmit SM (TSM) scheme and jointly optimize the UE’s power allocation matrix and the RIS reflection coefficients to enhance the system reliability. We formulate a non-convex optimization problem to reduce the system symbol-error-rate (SER) and propose a novel penalty-alternative optimizing algorithm to obtain a near-optimal solution. Following this, we show that with the assistant of RIS, receive SM (RSM) scheme can also be performed even if the UE has only one RF chain. Based on this observation, a novel RIS-assisted RSM scheme is proposed, which can provide a low cost and complexity solution for system realization. The reflection coefficients of the RIS are also optimized for the proposed RSM. Numerical results show that the RIS-assisted TSM can achieve a lower SER than the conventional communication scheme (CTS) without SM and the proposed RSM scheme has lower detection complexity than that of CTS. It is also shown that the performance of the TSM and RSM schemes is more sensitive to the quantization accuracy of the phase of the RIS coefficients than that of the amplitude.

To develop sustainable fifth generation (5G) wireless networks and utilize the unused spectrum, this paper focuses on cognitive radio (CR) based wireless information and energy transmissions from an unmanned aerial vehicle (UAV) to multiple low-power ground terminals (GTs). By practically considering the location-dependent air-to-ground (A2G) channel states and the non-linear energy harvesting (EH), we propose a dynamic fly-hover-transmit scheme, where the UAV successively flies between GTs, and hovers close to each GT for efficient wireless energy transfer (WET) or wireless information transfer (WIT) when the primary user (PU) is idle. By causally and optimally determining the UAV's mobility and transmit power for each selected transmission mode (WIT, WET, or being silent), we formulate the UAV's sum-throughput maximization over all GTs as a constrained Markov decision process (MDP) problem with battery energy constraints at all GTs and the UAV. Due to the infinitely large MDP system state space, this problem is difficult to solve. We then decompose this problem into two subproblems, by first deciding the UAV's transmission mode and power above a given GT, and then optimizing the UAV movement policy over multiple GTs. In the first subproblem, we propose an approximate to the complicated MDP value function of low complexity in closed-form, and then analytically derive the threshold-based suboptimal transmission policies. In the second subproblem, we optimally solve a simple-but-fundamental two-GT case, and then extend the general location-dependent GT weight design to an efficient suboptimal UAV movement policy. Simulation results show the significantly improved system performance under the proposed suboptimal policies over various benchmarks in dynamic networks.

This paper investigates a spectrum sharing based cognitive unmanned aerial vehicle (UAV) network. To protect the primary users (PUs) from harmful co-channel interference from the UAVs' transmissions, and at the same time, to control the resultant downlink interference at the secondary users (SUs), each UAV is equipped with a directional antenna of adjustable beamwidth. We adopt a probability-based air-to-ground (AtG) channel model to reflect both line-of-sight (LoS) and non-line-of-sight (NLoS) effects from the UAVs' transmissions to the users on ground. By studying the interference distributions in both primary network and cognitive UAV network, we successfully characterize the coverage probabilities for both PUs and SUs. We then optimally design the UAV density and the UAV beamwidth, so as to maximize the UAV coverage probability under a PU coverage probability constraint. Despite of the complicated network performance characterizations that make this problem non-convex, we find an efficient method to solve this problem. Finally, numerical results are provided to validate the theoretical analysis, and show the performance of our proposed spectrum sharing method via UAV beamwidth over the benchmark scheme.

In this paper, we investigate the uplink of a millimeter-wave (mmWave) communication system, in which small base stations (SBSs) are densely placed. The positions of the SBSs are modeled as a homogeneous Poisson point process (PPP) and a user equipment (UE) connects to multiple SBSs opportunistically and adopts the spatial modulation (SM) scheme. Two detection methods, namely the centralized detection and distributed detection, are first proposed for the SBSs to demodulate the received signal and the power allocation scheme minimizing the system symbol error rate (SER) is investigated. Following this, by ignoring the side lobe of the UE, the probability that the SM scheme outperforms the conventional transmission scheme in which the UE only communicates with the SBS with a stronger channel is approximated to evaluate the performance of the SM scheme. Numerical results show that the SM scheme can effectively improve reliability of the system.

Building an efficient, smart, and multifunctional power grid while maintaining high reliability and security is an extremely challenging task, particularly in the ever-evolving cyber threat landscape. The challenge is also compounded by the increasing complexity of power grids in both cyber and physical domains. In this article, we develop a stochastic Petri net based analytical model to assess and analyze the system reliability of smart grids, specifically against topology attacks under system countermeasures (i.e., intrusion detection systems and malfunction recovery techniques). Topology attacks, evolving from false data injection attacks, are growing security threats to smart grids. In our analytical model, we define and consider both conservative and aggressive topology attacks, and two types of unreliable consequences (i.e., system disturbances and failures). The IEEE 14-bus power system is employed as a case study to clearly explain the model construction and parameterization process. The benefit of having this analytical model is the capability to measure the system reliability from both transient and steady-state analysis. Finally, intensive simulation experiments are conducted to demonstrate the feasibility and effectiveness of our proposed model.

A joint user clustering and subcarrier allocation scheme for downlink non-orthogonal multiple access (NOMA) systems is examined in this paper. We propose the scheme for downlink NOMA systems which maximizes the achievable diversity order. The closed-form expression of the outage probability of the worst-performance user in the worst case is derived and validated by simulations. Numerical results show that the proposed scheme can achieve the same diversity order as the exhaustive searching scheme.

In this paper, we investigate physical layer security in a two-tier heterogeneous network with sub-6 GHz massive multi-input multi-output (MIMO) macro cells and millimeter wave (mmWave) small cells. By considering pilot attacks from the eavesdroppers, we analyze the coverage and secrecy performance using stochastic geometry. For the sub-6 GHz tier, we show that increasing the number of BS antennas is more effective than increasing BS density in improving the coverage performance, whereas densifying BS is more effective for security enhancement. For the mmWave tier, we first derive the success probability of beam alignment based on a beam sweeping-based channel training model. It is shown that the mmWave tier may outperform the sub-6 GHz counterpart in terms of both coverage and secrecy through densifying the base stations. Our results also reveal that the mmWave small cell can provide better coverage performance in the high transmission rate region, and can achieve higher security in the low redundant rate region, which reveals the advantage of using mmWave for secure communication. Numerical results verify the analysis.

In this paper, the impact of adaptive smarter eavesdroppers on the secrecy performance of a multi-antenna cellular network is investigated. The eavesdroppers can act as either passive eavesdroppers or active jammers based on their distance to the active base stations (BSs). To analyze the secrecy performance, the BSs, cellular users, and eavesdroppers are modeled as independent Poisson point processes. The closed-form expressions of the connection outage probability and lower bound of the secrecy outage probability are derived using the stochastic geometry approach. Following that, the conditions under which the eavesdroppers can act as active jammers are obtained. Finally, the optimal power allocation between artificial noise and information signal and the secrecy code rate at each BS, as well as the guard zone of the eavesdroppers are obtained using the Stackelberg game approach, where the eavesdroppers are modeled as the leader and the BSs as the follower. The Stackelberg equilibrium is obtained through the proposed iterative algorithm. Numerical results verify the theoretical analysis and show that the secrecy performance can be degraded severely by the adaptive eavesdroppers.

In this paper, the security issue in a multi- input-single-output (MISO) system in the presence of multiple randomly distributed eavesdroppers (Eves) is investigated. The Eves are distributed according to a Poisson Point Process (PPP) and each of them can operate in either a half-duplex (HD) or full-duplex (FD) mode. For a FD-mode Eve, it can overhear the secret information transmission and send some jamming signals simultaneously to degrade the reception of Bob. Based on the stochastic geometry method, we first derive the cumulative distribution function (cdf) expressions of the signal-to-interference-plus- noise ratio (SINR) for Bob and Eves. Following that, a lower bound of the intercept probability is obtained. Some important properties are derived, which provide very useful insights. Finally, the optimal power allocation of Alice and the optimal fraction of FD-mode Eves are solved by using a game-theoretical approach. Simulation results are shown to validate the theoretical analysis.

In this paper, we study a wireless-powered cooperative relaying system which consists of a source node (SN), a relay node (RN) and a destination node (DN). The RN has no embedded power supply, thus it needs to harvest energy from the radio signal transmitted by a power beacon (PB) which is responsible for charging the RN before forwarding the information to the DN. In addition, we assume that the RN possesses a buffer and can temporarily store the information received from the SN. Based on this assumption, we propose an adaptive transmission scheme in which the system adaptively switches between two transmission modes, namely the SN information transmission (SN-IT) mode and the RN harvest-and-transmit (RN-HAT) mode. The optimal mode adaptation method that maximizes the throughput of the system is obtained for different system setups and the throughput of the system is obtained in closed-form expressions. Numerical results are provided to verify the analytical expressions. It is shown that the throughput of the wireless-powered cooperative relaying system can be improved by using the proposed adaptive transmission scheme.

We consider a wireless-powered relaying system in which the relay nodes (RNs) that have no embedded power supply are charged by the radio frequency signal transmitted by a power beacon. Each RN is assumed to possess a buffer and can temporarily store the information received from the source node. Based on this assumption, we propose an adaptive transmission scheme, in which each RN adaptively switches between two transmission modes. To maximize the system throughput, a joint node-and-mode selection scheme in which the RN and its transmission mode are jointly selected in each time slot is investigated. For the general system setup, some analytical results are obtained, which provide useful insights in finding the optimal joint node-and-mode selection scheme. Following this, the analysis is applied to two specific systems, the single RN system and the multiple RNs system with a symmetric channel. For these system setups, the system throughput and the corresponding optimal transmission scheme are obtained in the closed-form expressions. Numerical results show that the throughput of the wireless-powered relaying system can be improved by using the proposed transmission schemes.

In this paper, we study a wireless powered co-operative communication system, which consists of a hybrid relay node (RN), a source node (SN), and a destination node (DN). It is assumed that the hybrid RN has a constant power supply while the SN has no embedded power supply. Thus, the SN needs to first harvest energy from the radio frequency (RF) signal broadcasted by the hybrid RN before transmitting information to the hybrid RN. By assuming that the RN has an information buffer and can temporarily store the information it received, we investigate the long-term throughput of two different block-wise cooperative protocols, namely the block-wise harvest-and-transmit (BW-HaT) protocol and the block-wise mode adaptation (BW-MA) protocol. For the BW-HaT protocol, the throughput expression is obtained in closed form. For the BW-MA protocol, the optimal mode adaptation method that maximizes the throughput of the system is presented and the maximum throughput is given for different system setups. It is shown that through simultaneously transmitting information and energy to the DN and SN, respectively, the proposed transmission schemes can significantly increase the system throughput.

In this paper, we consider a relaying system which consists of a multiple-antenna source node (SN), M single-antenna destination nodes (DNs) and a multiple-antenna relay node (RN). The RN possesses a buffer and it is able to store the decoded message before retransmitting the message to the DNs. We use the joint link-and-user scheduling method to maximize the long term average achievable rate of a multiple-input multiple-output (MIMO) relaying system. The optimal scheduling criteria is obtained and a two-step approach is proposed to implement it. In addition, we propose a rate allocation scheme of the source-relay (S-R) link to preserve the flow conservation constraint of each individual user. Furthermore, two reduced complexity joint link-and-user scheduling methods which use the zero forcing (ZF) beamforming in the relay-destination (RD) link are investigated. It is shown that joint link-and-user scheduling can significantly increase the average achievable rate of the system.

In this paper, we propose a buffer state based relay selection scheme for a finite buffer-aided cooperative relaying system. Our proposed relay selection scheme selects a relay node based on both the channel quality and the buffer state of the relay nodes. The Markov chain model is adopted to analyze the buffer state transition properties and the outage probability of the proposed relay selection scheme is obtained in a closed-form expression. Our analytical results show that if the buffer size of each relay node is greater than 2, the proposed relay selection scheme can achieve full diversity. Furthermore, it is shown that our proposed relay selection scheme has lower average packet delay compared with the max-link relay selection scheme. In addition, the average packet delay of the proposed relay selection scheme does not increase as the buffer size increases.

In this study, the authors investigate the diversity‐multiplexing tradeoff (DMT) of a wireless system in which user equipments (UEs) are equipped with only one antenna while the destination has multiple antennas. Each UE is assisted by M half‐duplex cooperative users serving as relay nodes. They combine the opportunistic relaying scheme and the cooperative spatial multiplexing (C‐SM) scheme to produce an opportunistic C‐SM (OC‐SM) scheme. The fraction of time allocated to the source–relay link and the relay–destination link is treated as a design parameter to minimise the outage probability of the system. At high signal‐to‐noise ratio regime, the optimal time allocated factor is obtained in a closed‐form expression through DMT analysis. Based on the DMT result of the proposed OC‐SM scheme, an adaptive OC‐SM (AOC‐SM) approach in which the system can switch between different C‐SM models adaptively based on the rate transmitted is proposed. To reduce the complexity of the relay nodes, an AOC‐SM scheme with relay selection (SAOC‐SM) in which a constant number of relay nodes is selected from the set of relay nodes that have successfully decoded the source message is investigated. The DMT results of the SAOC‐SM scheme and the AOC‐SM scheme are obtained and compared.

In this paper, we propose an orthogonal space-time block coding (STBC) for continuous phase modulation (CPM) over frequency-selective channels. The new method maintains the constant envelope and the phase continuity of the CPM waveforms by inserting three blocks of data-dependent symbols in addition to a cyclic prefix (CP). At the transmitter, the data blocks are precoded and transmitted in two antennas. And at the receiver, by applying the Laurent decomposition of CPM signals, only linear processing is needed to decode the CPM signal and the decoding complexity is much lower than traditional schemes. Simulation results corroborate that the proposed scheme achieves near-optimum error performances.